Coordination independent rate adaptation deployment methods and systems

ABSTRACT

A method and system of coordination independent rate adaptation is disclosed. The method and system of coordination independent rate adaptation provides for deploying a rate adaptation subdomain into a packet network having no rate adaptation coordination among the network nodes. The method and system is particularly useful for incremental introduction of rate-adaptive devices in existing networks; combining the best properties of sleep-state exploitation and rate scaling techniques by defining a class of state-setting policies for rate adaptation schemes that enforce tight deterministic bounds on the extra delay that the schemes may cause to network traffic at every node where they are deployed.

FIELD OF THE INVENTION

The invention relates to energy efficiency with rate adaptation and isparticularly concerned with rate-adaptation schemes that combine thebest properties of sleep-state exploitation and rate-scaling techniques.

BACKGROUND OF THE INVENTION

Data networking services and applications enable substantial energysavings in broad sectors of economic activity, for example by replacingthe transportation of people and goods with the electronic transfer ofdata packets. In order to maximize the energy-saving yields of thereplacement function offered by network services and applications,packet network themselves must target energy efficiency comprehensively,in every component and in every function. Energy efficiency thus becomesan attribute of foremost importance in the design and qualification ofnetwork equipment.

Network nodes forward data packets from one network interface toanother, often modifying part of their contents. To minimize energyconsumption, energy use in network equipment should be proportional totraffic handling activity. More specifically, energy use in a systemshould scale with the network traffic load and should become negligiblewhen the system does not process packets.

Rate scaling and sleep-state exploitation are popular methods forreducing energy consumption in network nodes when traffic load is wellbelow link capacity. (For reference, in this disclosure the terms ratescaling and sleep-state exploitation as referred to as examples of rateadaptation.)

With rate scaling, the clock frequency of the data-path device changesover time to track the traffic load, using the generally linearrelationship that exists between frequency and power to decrease powerconsumption levels under low traffic loads. However, since the trafficprocessing rate also scales with the operating frequency, delayaccumulation may be caused by frequency reductions. To control delayaccumulation, the operation of rate-scaling systems is typicallyconstrained by bounds on the additional delay that lower processingrates can impose on traffic. Steeper reductions of power consumptionlevels can be obtained by integration of dynamic voltage and frequencyscaling (DVFS) technologies in the rate-scaling system. In a DVFSdevice, the voltage level of the power supply can decrease as the clockfrequency scales down, at least down to the minimum voltage level neededto maintain the electronic circuitry of the device in predictableoperation.

With sleep-state exploitation, the network device alternates between afull-capacity state where it operates at maximum clock frequency as longas traffic is available for processing and a sleep state where powerconsumption is much lower.

While a significant body of work can be found in the literature thatdefines and studies rate-scaling and sleep-state exploitation schemesand architectures, several issues remain unresolved.

First, a clear framework for the coordination of contiguousrate-adaptation devices is not yet available. For sleep-stateexploitation techniques with the architectures that have been proposedso far, the lack of coordination may lead to substantial drops inenergy-saving performance, while the introduction of coordinationrequires the broad consensus of a standard body (see for example theongoing work within the IEEE 802.3az Energy Efficient Ethernet TaskForce) or the full cooperation of large portions of the entire network.Even within a single circuit pack, new components, and therefore newsources of energy consumption, must be added to coordinate the clockfrequency of multiple rate-scaling devices.

Second, while the energy-saving performance results presented forrate-adaptation techniques are generally encouraging, they heavilydepend on the specific set of operating states that are available in agiven device and on the resulting power-rate (PR) function. Whendifferent schemes are compared, the final choice for the best solutioncan only be determined after entering the actual parameters of apractical device. General guidelines for straightforward designpractices remain unavailable.

Therefore, it would be desirable to have a scheme for rate adaptationthat combine the best properties of sleep-state exploitation and ratescaling while overcoming the limitations of the rate-adaptationtechniques that are available in the prior art. [AF Question: should the“limitations” be spelled out explicitly here (e.g., need forcoordination, lack of a clear winner, lack of a clear trade-off betweenenergy savings and delay degradation)?]

SUMMARY OF THE INVENTION

An object of the present invention is to provide an improved system andmethod for achieving energy efficiencies in packet networks via rateadaptation.

According to an aspect of the invention there is provided a method ofestablishing a rate-adaptation domain in a packet network having aplurality of connected network nodes not providing rate adaptation, themethod having the steps of first, partitioning the plurality ofconnected network nodes into subdomains, each subdomain havingconnecting interfaces with at least one other subdomain. Then,identifying those rate independent connecting interfaces among theconnecting interfaces wherein the integrity of the data transfersthrough the connecting interfaces is preserved independent of theoperating states of those subdomains connected. Next, identifying atleast one subdomain for which all connecting interfaces are rateindependent connecting interfaces; and operating this subdomain as arate independent subdomain.

In some embodiments of the invention the operating step includesreceiving packets at an input interface from a domain immediatelyupstream to the rate-adaptation domain; and establishingQuality-of-Service requirements associated with the received packets.Further, processing the received packets in a processing unit;dispatching packets at an output interface to a domain immediatelydownstream from the rate-adaptation domain; establishing a set of statevariables; controlling the operating state of the processing unit withthe state variables so as to control the rate at which packets aredispatched over the output interface; selecting such state variables soas to satisfy a feasible departure curve; and further selecting suchstate variables so as to reduce energy consumption in therate-adaptation domain.

Advantageously, in certain embodiments the packet processing stepconsists of at least one of the group of parsing, modifying, and storingin a buffer queue. Also, in some embodiments the further selecting stepselects state variables so as to place the processing unit in a sleepstate or selects state variables so as to adjust the clock rate of theprocessing unit. As well, in some embodiments, the feasible departurecurve is an optimal departure curve.

According to another aspect of the invention, there is provided a systemhaving a rate-adaptation domain in a packet network having a pluralityof connected network nodes. The system includes a plurality ofsubdomains, each subdomain being a partitioned subset of the pluralityof connected network nodes, each subdomain having connecting interfaceswith at least one other subdomain. As well, the system includes a set ofrate independent connecting interfaces among the connecting interfaceswherein the integrity of the data transfers through the connectinginterfaces is preserved independent of the operating states of thosesubdomains connected; at least one subdomain for which all connectinginterfaces are rate independent connecting interfaces; and wherein theat least one subdomain operates as a rate-adaptation subdomain.

In some embodiments of the invention the at least one subdomain furtherincludes an input interface for receiving packets from a domainimmediately upstream to the rate-adaptation domain; a set ofQuality-of-Service requirements associated with the received packets; aprocessing unit for processing the received packets; an output interfacefor dispatching packets to a domain immediately downstream from therate-adaptation domain; a set of state variables for controlling theoperating state of the processing unit wherein the state variablescontrol the rate at which packets are dispatched over the outputinterface; a subset of such state variables selected so as to satisfy afeasible departure curve; and a further subset of the subset of statevariables selected so as to reduce energy consumption in therate-adaptation domain.

Advantageously, in certain embodiments the system further includes abuffer queue wherein the processing unit may store packets which havebeen at least one of the set of parsed and modified by the processingunit. As well, the state variables may be selected to control settingthe processing unit in a sleep state or adjusting the clock rate of theprocessing unit. In certain embodiments, the feasible departure curve isan optimal departure curve.

Note: in the following the description and drawings merely illustratethe principles of the invention. It will thus be appreciated that thoseskilled in the art will be able to devise various arrangements that,although not explicitly described or shown herein, embody the principlesof the invention and are included within its spirit and scope.Furthermore, all examples recited herein are principally intendedexpressly to be only for pedagogical purposes to aid the reader inunderstanding the principles of the invention and the conceptscontributed by the inventor(s) to furthering the art, and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Moreover, all statements herein recitingprinciples, aspects, and embodiments of the invention, as well asspecific examples thereof, are intended to encompass equivalentsthereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further understood from the followingdetailed description of embodiments of the invention, with reference tothe drawings in which:

FIG. 1 illustrates an example of network nodes connected by a data-pathin accordance with the known art;

FIG. 2 a illustrates a set of network nodes connected by bidirectionallinks in accordance with the known art;

FIG. 2 b illustrates an alternative version of FIG. 2 a in which thebidirectional links are split into their unidirectional components;

FIG. 2 c illustrates a directed graph version of FIG. 2 b in whichvertices represent rate-adaptation domains and edges representinterfaces between the domains;

FIGS. 3 a and 3 b illustrate a set of arrival and departure curves;

FIGS. 4 a and 4 b illustrate a set of optimal service curves for thearrival curves of FIGS. 3 a and 3 b respectively;

FIG. 5 is a plot illustrating a sequence of operating intervals for arate-adaptation domain according to an embodiment of the invention;

FIG. 6 is a plot illustrating different operating regions for a ratescaling device;

FIG. 7 is a plot illustrating power consumption for a sleep-stateexploitation device;

FIG. 8 is a plot illustrating power consumption for a hybrid combinationof a rate-scaling and sleep-state exploitation device;

FIG. 9 is a plot illustrating the optimal power function transitionpoint from sleep-state exploitation to rate-scaling according to anembodiment of the invention;

FIG. 10 is a comparison plot for the energy gains of several backlogclearing policies according to embodiments of the invention;

FIG. 11 is a further comparison plot for the extra delays of twospecific strong backlog clearing policies according to embodiments ofthe invention;

FIG. 12 is a performance plot showing the effects of a rate-adaptationscheme that combines rate-scaling with sleep-exploitation according toan embodiment of the invention; and

FIG. 13 is a comparison plot contrasting the delay performance of arate-adaptation scheme to its underlying rate-scaling scheme accordingto an embodiment of the invention.

DETAILED DESCRIPTION

Following are definitions of terms and concepts that are used throughoutthe description. Also described is the reference model that is used forevaluating the energy-saving performance of rate adaptation schemes.

A rate-adaptive networking device is a device that receives and forwardsdata packets over one or more interfaces and is capable of adjusting itsoperating state to the traffic load it is offered. The rate-adaptivedevice is arranged with a finite set S={s_(k),k=1 . . . K} of operatingstates, where each state is identified by fixed values of clockfrequency (f_(k)) and power-supply voltage (V_(k)): s_(k)=(f_(k),V_(k)).Since a unique traffic processing capacity r can be associated withevery frequency value f, we can represent the operating states also byreference to the respective processing capacities: s_(k)≡(r_(k),V_(k)).For each state s_(k), an active substate s_(k) ^(a), an idle substates_(k) ^(j), and a transition substate s_(k) ^(t) are further defined,depending on whether the device is processing traffic (s_(k) ^(a)), notprocessing traffic (s_(k) ^(j)), or transitioning to state s_(k) from adifferent state s_(j) (s_(k) ^(t)). In a first-order approximation ofthe energy model for rate-adaptive devices, fixed levels of powerconsumption (P_(k) ^(a), P_(k) ^(i), P_(k) ^(t)) are associated witheach substate. The three power consumption levels are referred to as theactive power, the idle power, and the transition power, respectively.

For a given device, it is assumed that the transition time δ, which isthe time needed to complete a state transition, is fixed and independentof the two states involved in the transition: δ(s_(j),s_(k))=δ∀(j,k). Asa conservative assumption, it is assumed that no traffic processing cantake place during a state transition.

Eq. (1) expresses the energy consumed over a time interval of duration Tbased on the time spent in each substate:

$\begin{matrix}{{E(T)} = {\sum\limits_{k = 1}^{K}\left( {{P_{k}^{a} \cdot T_{k}^{a}} + {P_{k}^{i} \cdot T_{k}^{i}} + {P_{k}^{t} \cdot T_{k}^{t}}} \right)}} & (1)\end{matrix}$

The idle power can comfortably be assumed to be always lower than theactive power: P_(k) ^(j)=β·P_(k) ^(a),0<β<1. Considering that the deviceremains idle during a state transition, it is reasonable to argue thatthe transition power P_(k) ^(t) should be defined as the average of theidle power for the states before and after the state transition, andtherefore as a function of both states: P_(k) ^(t)=(P_(j) ^(i)+P_(k)^(i))/2=P_(k) ^(t)(j,k). Observing that, under fluctuating load levels,for every transition into a state there is always a transition out ofthe same state, the inbound and outbound idle power contributions forstate s_(k) can be aggregated into a fixed transition power P_(k)^(t)=P_(k) ^(i) for the inbound state transition, and accordingly removethe idle-power contribution P_(k) ^(i)/2 from the transition power forthe outbound transition. Eq. (1) may now be rewritten as follows:

$\begin{matrix}{{E(T)} = {\sum\limits_{k = 1}^{K}\left\lbrack {{P_{k}^{a} \cdot T_{k}^{a}} + {P_{k}^{i} \cdot \left( {T_{k}^{i} + T_{k}^{t}} \right)}} \right\rbrack}} & (2)\end{matrix}$

Eq. (2) is the metric of reference for comparing the energy-savingperformance of different rate-adaptation schemes.

In a sleep-state exploitation device only two operating states areavailable: the full-capacity state s_(C)≡(C,V_(C)) and the sleep states_(S)=(0,V_(S)). The energy equation becomes as follows:

E(T)=P _(C) ^(a) ·T _(C) ^(a) +P _(C) ^(i)·(T _(C) ^(i) +T _(C) ^(t))+P_(S) ^(i)·(T _(S) ^(i) +T _(S) ^(t))  (3)

To take advantage of the reduced power consumption in sleep state, thedevice must transition out of the full-capacity state every time thereis no packet to be processed, so that T_(C) ^(i)=0:

E(T)=P _(C) ^(a) ·T _(C) ^(a) +P _(C) ^(i) ·T _(C) ^(t) +P _(S) ^(i)·(T_(S) ^(i) +T _(S) ^(t))  (4)

Sleep-state exploitation matches the average offered traffic load λ bylimiting the time spent in the full-capacity state:

$\begin{matrix}{\lambda = {C\frac{T_{C}^{a}}{T}}} & (5)\end{matrix}$

In a rate-scaling device, eq. (2) remains the energy equation ofreference, only with the restriction that no sleep state is included inthe set of operating states available. The device matches the offeredload by proper distribution of the time spent in each operating state:

$\begin{matrix}{\lambda = \frac{\sum\limits_{k = 1}^{K}{r_{k} \cdot T_{k}^{a}}}{T}} & (6)\end{matrix}$

The type of concavity of the power-rate function of a device shoulddrive the selection of the rate-adaptation scheme: if the function isconcave over the full set of available processing capacities, i.e.,((P(r₁)+P(r₂))/2≦P((r₁+r₂)/2)∀r₁<r₂, the adoption of sleep-stateexploitation is the obvious choice, if effective at all in reducingenergy consumption. If instead the curve is convex, i.e.,(P(r₁)+P(r₂))/2≧P((r₁+r₂)/2)∀r₁>r₂, a properly designed rate-scalingscheme can yield higher energy savings.

Rate-Adaptation Representation of Packet Networks

It has been historically argued that tight coordination between adjacentnetwork nodes is necessary for certain rate-adaptation techniques toyield any energy-saving gains. The need for coordination hinders thedeployment of those techniques because no new technology can bereasonably expected to be exhaustively deployed overnight in anymulti-player environment. The following development shows via a simpledata path abstraction that the need for coordination can be entirelyremoved and that rate-adaptive devices can be deployed incrementally,both within individual network nodes and in the network at large.

Referring to FIG. 1, there may be seen an example of the simplestdata-path configuration 100, wherein a rate-adaptive device 102 forwardspackets to a rate-adaptive device 104 over an interface 106.

Devices A and B are arranged with respective sets of rate-adaptationoperating states, possibly identical. The nature of interface Cdetermines whether the states of devices A and B must be controlledjointly or can be controlled separately:

-   -   If interface C requires both devices to be in consistent        rate-adaptation states in order for the integrity of any data        transferred over C to be preserved, the states of devices A and        B must be controlled jointly. This would be an example of a case        wherein devices A and B belong to the same rate-adaptation        domain. As an example of equipment, the transmitter and receiver        at the two ends of an optical fiber can be seen as parts of the        same rate-adaptation domain.    -   If the integrity of data transfers over interface C is preserved        independently of the operating states of devices A and B, there        is no need for coordination between the two devices, which thus        belong to distinct rate-adaptation domains. As an example, two        devices that are separated by a buffer memory in the packet        processing pipeline of a router's line card belong to distinct        rate-adaptation domains and their operating states do not        strictly require coordination as long as the buffer memory can        be accessed at different speeds for write and read operations.

As a more formal definition, a rate-adaptation domain may consist of oneinput interface where packets are received from the domain immediatelyupstream, a processing unit where packets may be parsed, modified, orassigned to buffer queues, and one output interface through whichpackets are dispatched to the domain immediately downstream. Therate-adaptation domain maintains state variables that drive theoperating state of the processing unit and therefore the rate at whichpackets are forwarded over the output interface. For stability purposes,it is assumed that the maximum transmission capacity of the outputinterface and the maximum processing capacity of the processing unit areidentical and never lower than the maximum capacity of the inputinterface.

Referring to FIG. 2 a there may be seen three network nodes n₁ 201, n₂202, and n₃ 203 connected by bidirectional links L1 206 and L2 208. Noden₂ 202 may for example be a router with output-queued 2×2 switch fabric.Referring to FIG. 2 b, it may be seen that each bidirectional link issplit into its two unidirectional components (L1 206 splits into I_(1,2)212 and I_(2,1) 221, L2 208 splits into I_(2,3) 223 and I_(3,2) 232.This representation may be further elaborated as shown in the directedgraph of FIG. 2 c, wherein the vertices v₁ 271 through v₆ 276 representrate-adaptation domains and the edges e₁ 261 through e₅ 265 representthe interfaces between them.

More specifically:

-   -   vertex v₁ 271 includes the n₁ 201 transmitter and the n₂ 202        receiver at the two ends of link I_(1,2) 212;    -   edge e₁ 261 is the portion of packet memory in n₂ 202 that is        dedicated to packets arriving from link I_(1,2) 212;    -   vertex v₂ 272 includes the n₃ 203 transmitter and the n₂ 202        receiver at the two ends of link I_(3,2) 232;    -   edge e₂ 262 is the portion of packet memory in n₂ 202 that is        dedicated to packets arriving from link I_(3,2) 232;    -   vertex v₃ 273 is the interface aggregation module in n₂ 202 that        multiplexes packet streams from links I_(1,2) 212 and I_(3,2)        232;    -   edge e₃ 263 is the buffer where packets from different input        links are lined up into a single queue;    -   vertex v₄ is the header parsing module in n₂ 202, which assigns        packets to their respective egress queues and prepares them for        modification prior to transmission;    -   edge e₄ 264 is the buffer where packets waiting for transmission        to node n₁ 201 are queued;    -   edge e₅ 265 is the buffer where packets waiting for transmission        to node n₃ 203 are queued;    -   vertex v₅ includes the n₂ 202 transmitter and the n₁ 201        receiver at the two ends of link I_(2,1) 221; and    -   vertex v₆ includes the n₂ 202 transmitter and the n₃ 203        receiver at the two ends of link I_(2,3) 223.

In the design of a network system with rate adaptation capabilities, thedirected-graph representation of the system helps identify opportunitiesfor the insertion of rate-adaptive devices. As well, the directed-graphrepresentation allows a recognition of trade-offs between the benefitsof designs with a small number of rate adaptation domains (lower delaypenalties) and designs with a higher number of domains (lowercomplexity). The directed-graph representation indeed makes it evidentthat tight coordination between network nodes is not a necessarycondition for the introduction of rate-adaptation capabilities in apacket network. Rate-adaptive devices that single-handedly form a rateadaptation domain can be added to the network incrementally, withoutrequiring coordinated efforts from equipment vendors and serviceproviders. It becomes apparent, for example, that a system designer caninclude a rate-adaptive network processor in a line card that has norate-adaptive transceivers.

The following disclosure focuses on methods for controlling theoperating state of a rate-adaptation domain that fully avoid anycoordination between domains.

Implementation Guidelines for Rate-Adaptation Schemes

The following discussion identifies general guidelines for the effectiveimplementation of rate-adaptation schemes.

Consider a rate-adaptation domain in isolation. Through itsstate-setting policy, a rate-adaptation scheme sets the sequence ofoperating states in response to a sequence of packet arrivals with theobjective of minimizing energy consumption in the domain while notviolating the quality-of-service (QoS) requirements of the incomingtraffic. Different types of traffic may present different types of QoSrequirements, expressed through respective QoS constraints.

Listed here are a number of useful definitions and implications:

-   -   An arrival curve A(t), t≧0, is a right-continuous function that        counts the number of bits that have arrived to the        rate-adaptation domain in time interval [0,t]. in a practical        non-fluid domain, where bits are grouped in packets and all bits        of a packet are considered available for processing only after        the last bit has arrived, A(t) is a staircase function.    -   A departure curve D(t), t≧0, is a right-continuous function that        counts the number of bits that have departed from the        rate-adaptation domain in time interval [0,t]    -   For a given arrival curve A(t), a minimum departure curve        D_(min)(t) is a departure function such that D_(min)(t)≦A(t),        t≧0 and is defined as the minimum number of bits that must        depart by time t to satisfy the QoS requirements of the        rate-adaptation domain.

To meet the QoS requirements of the incoming traffic, the sequence ofoperating states set by the rate adaptation scheme must produce adeparture curve that always lies between the minimum departure curve(Q0S constraint) and the arrival curve (causality constraint):

A(t)≧D(t)≧D _(min)(t),(t)≧0.

Such a departure curve is called feasible. Referring to FIG. 3 a, theremay be seen a minimum departure curve D_(min)(t) and a feasibledeparture curve D(t) for a given arrival curve A(t) under two types ofdelay-based QoS constraint.

In the example of FIG. 3 a, the QoS constraint requires that all packetsthat arrive between time 0 and time T be delivered by time T . Thefeasible departure curve shown in the diagram derives from a sequence ofthree operating states s₁,s₂, and s₃ that have increasing processingcapacity. In the case of FIG. 3 b, a maximum delay d is accepted for thetransmission of every packet. The feasible departure curve shown isobtained from the sequence of two operating states s₁ and s₂ where thefirst state has null processing capacity.

It is known that for systems with convex power-rate function thedeparture curve that minimizes energy consumption, called the optimaldeparture curve D_(opt)(t), is the shortest feasible curve between thepoints (0,A(0)) and (T,D_(min)(t)). For quick visualization of theresult, the following has been suggested:

“Consider a string that is restricted to lie between A(t) andD_(min)(t). Tie its one end at the origin and pass the other end throughD_(min)(t). Now if the string is made taut, its trajectory is the sameas the optimal curve.”

FIGS. 4 a and 4 b show the optimal service curves for the two examplesof FIGS. 3 a and 3 b respectively (it should be noted that instantaneousstate transitions are assumed far the depiction of the curves of FIG. 3and FIG. 4).

The “shortest-string result” for convex power-rate functions indicatesthat energy minimization is achieved when the processing capacity of therate-adaptation domain is set equal to the average rate needed to meetthe QoS constraint exactly by the time available to complete the task.

The following differences can be appreciated between a practicalrate-adaptation domain and the more abstract reference system for theresults shown in FIG. 4:

1. In the abstract reference system, the operating state can be chosenout of an infinite set, with a continuous range of processing capacitiesavailable; in the practical rate-adaptation domain the set of operatingstates is finite, with discrete values of processing capacity available.As a consequence, the optimal departure curve for the practical domaintypically yields lower energy savings than the optimal curve for theideal system.

2. In the abstract system all state transitions are instantaneous; inthe practical domain, state transitions complete within a fixed amountof time, during which no traffic processing can take place.

3. In the abstract system, which processes a continuous stream of bits,state transitions can occur at any time. In the practical domain, whichprocesses bits in atomic packets, state transitions can only occur atpacket processing boundaries.

4. In the abstract system the arrival curve A(t) is known, so that theminimum departure curve D_(min)(t) can be tailored around it before thetraffic starts arriving; in the practical domain the arrival curve isunknown.

It is desired to implement a practical rate-adaptation scheme that neverviolates a given delay constraint d for all packets that traverse asingle rate-adaptation domain. In approaching the implementation, thediscussion is developed along the following three dimensions:

-   -   1. subdivision of the time axis into operating intervals;    -   2. optimization of the reference power-rate function; and    -   3. specification of the state-setting policy.

Operating Intervals

The extended duration of state transitions in the practicalrate-adaptation domain motivates the subdivision of the time axis intointervals of variable duration but never shorter than a fixed amount h,called the minimum hold time. In fact, the time cost δ of a statetransition implies that frequent state transitions must be deterred toprevent them from occupying most of the operating time of therate-adaptation domain. If the domain is allowed to spend substantialportions of its operating time switching states, then it must executeall traffic processing work within shorter time intervals, at higherpower levels. Even without counting the energy consumed during worklesstransitions, with a convex power-rate function the simple deviation ofthe operating state from the optimal average-rate point causessignificant energy losses. Forcing the operating state of the domain toremain unchanged for a minimum hold time h>>δ keeps the total transitiontime below an acceptable fraction of the total operating time.

Our target rate-adaptation scheme divides the operation of therate-adaptation domain into a sequence of operating intervals(t_(n),t_(n+1)] of variable duration h_(n), h_(n)>h . The operatingstate (and processing capacity) in any operating interval is alwaysdifferent than the state (and capacity) in the interval that immediatelyfollows. These relations may be seen by reference to FIG. 5.

With reference to a generic operating interval (t_(n),t_(n+1)], therate-adaptation scheme decides to set a different operating state attime t_(n). At time t_(n)+δ the state transition is complete and therate-adaptation domain can start processing traffic again. Starting attime t_(n)+h, the rate-adaptation domain checks at every packetprocessing completion if the state-setting policy requires the operatingstate to be changed. The end of the operating interval is reached attime t_(n+1)=t_(n)+h_(n)≧t_(n)+h, when the rate-adaptation domaincompletes the processing of a packet and the state-setting policyindicates the necessity of a state change.

Optimization of the Power-Rate Function

The power-rate function of a rate-adaptation domain defines therelationship between processing capacity and power consumption in theactive substate. In a practical device the power-rate function istypically defined over a finite set of discrete capacity values.However, for convenience of presentation and after considering that thecomposition of the set of discrete capacity values of the practicaldomain constitutes itself a design choice, this section refers topower-rate functions that are defined over continuous ranges of capacityvalues.

The current lack of commercial rate-adaptive network devices that canquickly adjust their operating state to traffic load conditions makesfor a lack of real-life numbers for key parameters such as the statetransition time and the power-rate function. However, sleep-stateexploitation devices and rate-scaling devices that incorporate DVFScapabilities to offer an extended set of operating states, onlycontrolled over a larger timescale, do exist (especially general-purposeprocessors [INTEL_(PXA), INTEL_(PENTIUM)]) and can be used to obtain areasonable indication of what future devices can accomplish.

Focusing now on DVFS-capable rate-scaling devices, a commonly acceptedconclusion about their power-rate function is its partition into twodifferent operating regions. These two different operating regions maybe seen in FIG. 6, where a finite set of operating states is explicitlymarked. In the upper portion of the capacity range the function isevidently convex, which motivates the deployment of rate-scalingcapabilities. However, the benefits of rate scaling become less evidentin the lower portion of the capacity range, especially for very lowoperating frequencies. While the capacity threshold that marks thebehavioral discontinuity obviously changes across devices, in all casesthe loss of energy-saving benefits is such that it makes no sense to setthe operating state of the device in the low-capacity region. As aconsequence, the energy efficiency of the rate-scaling device isexcellent when heavier traffic loads keep it in operation in thehigh-gain region, but becomes poor under lighter traffic loads.

It is relevant now to consider the equivalent power-rate function of asleep-state exploitation device whose operation alternates between thefull-capacity state s_(C)≡(C, P_(C)) and the sleep state s_(S)(0,P_(S)). In the ideal case of a device with instantaneous statetransitions (δ=0) the equivalent power-rate function is a continuousfunction where the operating state of the device over an interval ofduration T is derived from the time T_(C) spent in the full-capacitystate as follows: P_(eq)(T)=[P_(C)·T_(C)+P_(S)·(T−T_(C))/T. Refer toFIG. 7.

If a rate-scaling device and a sleep-state exploitation device withidentical full-capacity state s_(C)(C,P_(c)) are compared, and thecondition holds that that the minimum-capacity power P₀ in the former ishigher than the sleep-state power P_(S) in the latter, referring to FIG.8 it can easily be observed that the sleep-state exploitation deviceyields better energy savings in the lower portion of the capacity range(r<r_(T) where r_(T) is the value of capacity where the two curvesintersect), while the rate-scaling device prevails in the upper portion(r>r_(T)).

FIG. 8 suggests that an optimal power-rate function can be found in apractical rate-adaptation device that resorts to sleep-stateexploitation under light loads (i.e., for traffic arrival rate belowr_(T)) and to rate scaling under heavier loads (i.e., for trafficarrival rate above r_(T)). While it is true that a device with such ahybrid power-rate function would improve over the underlyingrate-scaling and sleep-state exploitation devices, an even better hybridpower-rate function can be defined based on the observation that theoptimal power-rate function is one that starting from the power-ratefunction of the available rate-scaling technology minimizes the areaunder its graph. In fact, the minimum-area condition defines thefunction that associates the minimum power level possible with eachoperating state available. The curve for such function is obtained byjoining the two components at the single point (r_(T,opt), P_(T,opt))where the tangent to the rate-scaling curve intersects the P axis in(0,P_(S)). In fact, by definition of convex curve the tangent to thecurve that includes a fixed point (0,P_(S)) below the curve is also thestraight line with minimum slope out of all straight lines by the samepoint that ever intersect the convex curve.

FIG. 9 illustrates graphically why such a definition of(r_(T,opt),P_(T,opt)) is the one that minimizes energy consumption:whether there is a move of the processing capacity of the joining pointlower (r_(T,j)<r_(T,opt)) or higher (r_(T,h)<r_(T,opt)), the result endsup with a larger area under the hybrid curve. In mathematical form,r_(T,opt) is defined as the value of processing capacity such that:

$\begin{matrix}{{\frac{}{r}{P_{rs}\left( r_{T,{opt}} \right)}} = \frac{{P_{rs}\left( r_{T,{opt}} \right)} - P_{S}}{r_{T,{opt}}}} & (7)\end{matrix}$

where P_(rs)(r) is the power-rate function of the original rate-scalingdomain.

Specification of the State-Setting Policy

The state-setting policy is the algorithmic core of the rate-adaptationscheme. It sets the time and the destination of each state transition,based on system parameters (power-rate function, transition time δ, andminimum hold time h), QoS constraints (maximum added delay d), and statevariables (queue length Q and traffic arrival measures).

First addressed is the determination of the time when a state transitionshould occur. In a previous section it was established that, in order toabsorb the cost of a state transition, the domain should remain in theresulting state for at least a time h after starting that transition.During the operating interval, the processing capacity of the domainremains unchanged.

In setting the value of h, the tradeoff is between extending h tominimize the negative effect of state transitions (an increase ofaverage processing rate by up to h/(h−δ) is needed to compensate theloss of processing capacity caused by a transition time δ), andcontracting h to improve the accuracy of the departure curve D(t) intracking the arrival curve A(t) (the larger h, the longer the time thetwo curves have to diverge). Since the delay constraint d expresses themaximum accepted distance between arrival and departure curve, it may beused to drive the specification of h.

The following definition is an important property that drives theclassification of state-setting policies.

Weak Backlog Clearing Property—At the beginning t₀ of the operatinginterval the state-setting policy chooses the processing capacity fromthe set of available capacities that are sufficient to clear the currentqueue backlog Q(t₀)>0 by the closest possible end of the operatinginterval (t₁=t₀+h): r(t₀)ε{r_(k):s_(k)εS

r_(k)≧Q(t₀)/h−δ)}. If no such capacity is available, the policy choosesthe full capacity of the rate adaptation domain: r(t₀)=C.

A state-setting policy that holds the weak backlog clearing property isreferred to as a weak backlog clearing policy. It should be noted thatthe weak backlog clearing property has no bearing over the case withnull backlog (i.e., when Q(t₀)=0).

The following theorem gives the relationship between d and h in a fluidrate-adaptation domain, where bits arrive in a continuous stream withoutpacket boundaries.

Theorem 1—In a fluid rate-adaptation domain with weak backlog clearingpolicy the delay constraint d is bounded as follows:

d<d _(max)=2h+δ  (8)

Proof: Without loss of generality, let t₀ be the start time of anoperating interval where the state-setting policy chooses a processingcapacity r(t₀) such that r(t₀)>Q(t₀)/(h−δ). At time t₁=t₀+h, when therate-adaptation domain has the next chance to set the processingcapacity, the queue backlog is Q(t₁)=Q(t₀)+A(t₀,t₁)−r(t₀)·(h−δ)≦C·h,where A(t₀,t₁)≦C·h is the number of bits received between t₀ and t₁. Attime t₁, the domain sets a processing capacity r(t₁) such that eitherr(t₁)≧Q(t₁)/(h−δ) if Q(t₁)≦C·(h−δ), or r(t₁)=C if Q(t₁)>C·(h−δ).

The former case is trivial because it reproduces the conditions at timet₀, only with a rate that is possibly different.

In the latter case, the queue backlog at the next useful time (t₂) forsetting the processing capacity isQ(t₂)=Q(t₁)+A(t₁,t₂)−C·(h−δ)≦2C·h−C·(h−δ)=C·(h+δ). At time t₂, ifQ(t₂)≦C·(h−δ) the domain returns to the condition of time t₀. Otherwisethe processing capacity remains C, so that no state transition istriggered.

At this point there is no way for the queue length to grow any furtherbeyond Q_(max)=C·(h+δ). If Q(t₂)=C·h+ΔB, ΔB>0 and no more trafficarrives after t₂, it takes up to two full operating intervals to clearthe backlog Q(t₂): the first operating interval takes the backlog downto Q(t₃)=ΔB and the second interval guarantees that the backlog iscleared by t₄=t₃+h. The earliest time the last bit cleared by t₄ mayhave arrived so that ΔB>0 is t_(ε)=t₂−δ+ε₁ε→0, so that d=t₄−t_(ε)<2h+δ.

The scenario at time t₂ in the proof of Theorem 1 leads to the followingformulation of a backlog clearing property that provides a tighter delaybound for given values of h and δ:

Strong Backlog Clearing Property—The processing capacity set at thebeginning t₀ of the operating interval is chosen from the set ofavailable capacities that are sufficient to clear the current queuebacklog Q(t₀)>0 by the closest possible end of the operating interval(t₁=t₀+h) and are higher than the current capacity r(t₀ ⁻):r(t₀)ε{r_(k):s_(k)εS

r_(k)≧Q(t₀)/(h−δ)

r_(k)≧r(t₀ ⁻)}.

If no such capacity is available, the policy chooses the full capacityof the rate adaptation domain (r(t₀)=C).

A state-setting policy that holds the strong backlog clearing propertyis referred to as a strong backlog clearing policy.

The following theorem defines the delay bound for a fluidrate-adaptation domain with strong backlog clearing policy.

Theorem 2—In a fluid rate-adaptation domain with strong backlog clearingpolicy the delay constraint d is bounded as follows:

d≦d _(max) =h+δ.  (9)

Proof Consider the same sequence of events as in the proof of Theorem 1,up to time when a backlog Q(t₂)=C·h+ΔB,ΔB>0 has accumulated in thequeue. As opposed to the case with weak backlog clearing policy, theprocessing capacity of the rate-adaptation domain cannot be reducedagain until the queue backlog is completely cleared. As a consequencer(t₂)=C and no additional backlog is allowed to accumulate in the queue.The maximum queue backlog remains Q_(max)=C·(h+δ) and d_(max)=h+δ is themaximum time it takes to forward the last bit in the queue after itarrives.

The following theorem defines the maximum queue length in a packetizedrate-adaptation domain with strong backlog clearing policy.

Theorem 3—In a packetized rate-adaptation domain with strong backlogclearing policy, the queue size Q(t) is bounded as follows:

$\begin{matrix}{{{Q(t)} \leq Q_{\max}^{(P)}} = {{C \cdot \left( {h + \delta} \right)} + {L_{\max} \cdot \left( {\frac{C}{r_{\min}} - 1} \right)}}} & (10)\end{matrix}$

where L_(max) is the maximum size of a packet in the rate adaptationdomain and r_(min) is the minimum non-null rate in the set of availablerates (r_(min)=min_(s) _(k) _(εS){r_(k):r_(k)>0}).

Proof: Without loss of generality, let t₀ be the start time of anoperating interval where the state-setting policy chooses a processingcapacity r(t₀) such that r(t₀)≧Q(t₀)/(h−δ). At time t₁, when thepacketized rate-adaptation domain has the next chance to set theprocessing capacity at the end of a packet transmission(t₀+h≦t₁<t₀+h+L_(max)/r(t₀)), the queue backlog isQ(t₁)=Q(t₀)+A(t₀,t₁)−r(t₀)·(t₁−t₀−δ), with A(t₀,t₁)≦C·(t₁−t₀).Therefore:

Q(t ₁)≦(C−r(t ₀))·(t ₁ −t ₀)+Q(t ₀)+r(t ₀)·(h−h+δ)

∴Q(t ₁)≦(C−r(t ₀))·(h+L _(max) /r(t ₀))+r(t ₀)·h

∴Q(t ₁)≦C·h+L _(max·() C/r _(min−)1)

Since Q(t₁)>C·(h−δ), at time t₁ the state-setting policy chooses r(t₁)=Cand during the subsequent state transition the rate-adaptation domainaccumulates up to C·δ more bits. At time t₁+δ, whenQ(t₁)≦C·h+L_(max)·(C/r_(min)−1), operation at the full capacity startspreventing the queue length from growing any further.

As shown in the proof of Theorem 2, in a rate-adaptation domain withstrong backlog clearing policy the maximum delay is the time needed toclear the maximum backlog at full capacity. The following resultimmediately derives from Theorem 3:

Lemma 1: In a packetized rate-adaptation domain with strong backlogclearing policy the delay constraint d is bounded as follows:

$\begin{matrix}{{d \leq d_{\max}^{(P)}} = {h + \delta + {L_{\max} \cdot \left( {\frac{1}{r_{\min}} - \frac{1}{C}} \right)}}} & (11)\end{matrix}$

The identification of the selection criterion for the operating state ofthe rate-adaptation domain completes the specification of thestate-setting policy. To retain the result of Lemma 1 on therelationship between the maximum added delay d_(max) ^((P)) and theminimum hold time h the selection criterion must satisfy the strongbacklog clearing property.

The simplest strong backlog clearing policy that also minimizes energyconsumption is one that chooses capacity

${r\left( t_{0} \right)} = {\min\limits_{s_{k} \in S}\left\{ {r_{k} :: {{r_{k} \geq {{Q\left( t_{0} \right)}/\left( {h - \delta} \right)}}{r_{k} \geq {r\left( t_{0}^{-} \right)}}}} \right\}}$

when Q(t₀)>0 and capacity

${r\left( t_{e} \right)} = {\min\limits_{s_{k} \in S}\left\{ {r_{k} :: {r_{k} < {r\left( t_{e}^{-} \right)}}} \right\}}$

at any time t_(e) such that Q(t_(e))=0 (i.e., chooses the processingcapacity immediately lower than the current one as soon as the queuegets empty, whether or not the duration of the current operatinginterval has passed the h mark). It should be noted that in bothstate-setting events the policy may find no capacity that satisfies thecorresponding condition. In that case the policy retains the currentcapacity without triggering a state transition. It should also be notedthat in a rate-adaptation scheme that combines rate scaling withsleep-state exploitation the set S of available operating states isformed by the union of the sleep state with the subset of states in theunderlying rate-scaling scheme such that the associated processingcapacities are not lower then the optimal threshold capacity r_(T,opt):

S={s_(S)}∪{s_(k)εS′:r_(k)≧r_(T,opt)} where S′ is the set of operatingstates in the underlying rate-scaling scheme and r_(s)=0 is the capacityassociated with the sleep state. While the policy only allowssingle-step capacity decrements when the queue becomes empty to avoidwide state oscillations along the convex power-rate curve, the samepolicy imposes no restrictions on state transitions that cause capacityincrements: at time t₀, when Q(t₀)>0 and a new operating interval canstart because at least h time has elapsed since the beginning of thecurrent operating interval, the policy chooses the new processingcapacity out of the full subset of available capacities that are notlower than the current one. The name for this state-setting policy isthe simple strong backlog clearing (SSBC) policy.

To improve statistically the delay performance of the rate-adaptationdomain, a predictor Â(t) for the traffic arrival rate can be included inthe state-setting policy. The simplest predictor uses the arrival rateof an operating interval as the arrival rate estimate for the nextinterval: Â(t_(n))=[A(t_(n+1))−A(t_(n))]/(t_(n+1)−t_(n)), where t_(n)and t_(n+1) are the start and end times of the n^(th) operatinginterval. While the combination of the current queue length with anestimate of the arrivals for the next operating interval does improvethe average delay performance of the rate adaptation domain, it cannotreduce the worst-case added delay of eq. (11). This is because apredictor, however accurate, has no actual control over future arrivals.Heretoafter are described the effects of including the simplestpredictor in the state-setting decision at time t₀ such that Q(t₀)>0

$\left( {{r\left( t_{0} \right)} = {\min\limits_{s_{k} \in S}\left\{ {r_{k} :: {{r_{k} \geq {\left\lbrack {{Q\left( t_{0} \right)} + {{\hat{A}\left( t_{0} \right)} \cdot h}} \right\rbrack/\left( {h - \delta} \right)}}{r_{k} \geq {r\left( t_{0}^{-} \right)}}}} \right\}}} \right)$

and in the state-setting decision at time t_(e) such that Q(t_(e))=0

$\left( {{r\left( t_{e} \right)} = {\max\limits_{s_{k} \in S}\left\{ {r_{k} :: {{r_{k} < {r\left( t_{e}^{-} \right)}}{r_{k} \geq {\hat{A}\left( t_{e} \right)}}}} \right\}}} \right).$

The name for this state-setting policy is the predictive strong backlogclearing (PSBC) policy.

A slightly different approach to the combination of current state (thequeue length Q(t)) and future arrivals (the predictor Â(t)) is derivedfrom a rate-scaling method of the prior art [Nedevschi]. Instead ofbeing projected over the entire operating interval to follow, thepredictor for future arrivals is only applied to the time needed tocomplete the next transition, so that the new processing capacity is setbased on the expected backlog at the time when the domain can startprocessing packets again. At time t₀, such that Q(t₀)>0 this partiallypredictive strong backlog clearing (PPSBC) policy sets the processingcapacity r(t₀) for the next operating interval as follows:

${r\left( t_{0} \right)} = {\min\limits_{s_{k} \in S}{\left\{ {r_{k} :: {{r_{k} \geq {\left\lbrack {{Q\left( t_{0} \right)} + {{\hat{A}\left( t_{0} \right)} \cdot \delta}} \right\rbrack/\left( {h - \delta} \right)}}{r_{k} \geq {r\left( t_{0}^{-} \right)}}}} \right\}.}}$

At time t_(e) such that Q(t_(e))=0 the PPSBC policy sets the processingcapacity r(t_(e)) for the next operating interval as follows:

${r\left( t_{e} \right)} = {\max\limits_{s_{k} \in S}{\left\{ {r_{k} :: {{r_{k} < {r\left( t_{e}^{-} \right)}}{r_{k} \geq {\hat{A}\left( t_{e} \right)}}}} \right\}.}}$

The discussion to this point has covered state-setting policies thathold the strong backlog clearing property. However, the experimentalresults presented in the following section also account for theperformance of weak backlog clearing policies and of a non-backlogclearing policy.

Experimental Results

The energy-saving and delay performance of rate-adaptation schemes thatincorporate the state-setting policies heretofore defined (SSBC, PSBC,and PPSBC) were studied by simulation of a 10 Gbps single-queuerate-adaptation domain fed by 10 independent Pareto sources, eachcapable of supplying up to 1 Gbps traffic. The sources generate 1000Byte packets and have average burst time and average idle time of 50 ms.The packet-level simulation experiments were run using newly designedQueue subclasses for the ns2 simulation platform. In every experimentdata were collected for 100 seconds of system time after expiration of a5 second warm-up period.

The power-rate curve of the domain was derived by interpolation andnormalization of the six operating states of the Intel® Pentium® MProcessor as documented in the March 2004 Whitepaper, “Enhanced Intel®SpeedStep® Technology for the Intel® Pentium® M Processor” (which may befound at ftp://download.intel.com/design/network/papers/30117401.pdf):

$\begin{matrix}{{P(r)} = \left\{ \begin{matrix}{P_{0},} & {r < r_{0}} \\{{P_{0} + {A \cdot \left( {r - r_{0}} \right)^{3}}},} & {r \geq r_{0}}\end{matrix} \right.} & (12)\end{matrix}$

where r₀=1.625 Gbps, P₀=0.232 W, and A=0.001308 W Gbps⁻³. The operatingstates of Table 1 were assumed to be available to the state-settingpolicy.

It is assumed that the sleep power P_(S) is a fraction of the baseactive power P₀ (P_(S)=γ·P₀, 0<γ<1) and that the idle power is afraction of the active power: P_(k) ^(i)=β·P_(k) ^(a)∀k, 0<β<1). Per eq.(7), the optimal threshold capacity is a function of the sleep powerP_(S) and therefore of the ratio γ between sleep power and base activepower. With the power-rate curve of eq. (12), a relatively low value ofthe ratio of (γ=0.125) yields r(t_(T,opt))=5.21 Gbps, whereas arelatively high value (γ=0.5) yields r(t_(T,opt))=4.51 Gbps. As acompromise between the two values, the minimum non-null capacityavailable in the rate-adaptation domain was set to 5 Gbps:

S={s_(S)}∪{s_(k)εS′:r_(k)≧5 Gbps}={s_(S),s₅,s₆,s₇,s₈,s₉,s₁₀}

The transition time δ is set at 1 ms knowing that it is well within thecapabilities of existing technologies but still a cause of delays thatmay affect the user perception of end-to-end applications. In theevaluation of rate-scaling schemes that do not include sleep-stateexploitation, the minimum non-null capacity available is set at 2 Gbps(Table 1 shows that there is no energy benefit in including s₁ in theset of available operating states).

TABLE 1 Processing Normalized Active State Capacity [Gbps] Power [W]s_(S) 0 (sleep) P_(s) s₁ 1 0.232 s₂ 2 0.232 s₃ 3 0.236 s₄ 4 0.249 s₅ 50.282 s₆ 6 0.341 s₇ 7 0.435 s₈ 8 0.571 s₉ 9 0.756 s₁₀ 10 1.000

Referring to FIG. 10 there may be seen the energy gains achieved in arate-scaling context by the three strong backlog clearing policiesdescribed previously (SSBC, PSBC, and PPSBC), by their weak backlogclearing counterparts (SWBC, PWBC, and PPWBC), and by a sleep-stateexploitation (SSE) domain (γ=0.125), with respect to a domain that onlyalternates between active and idle substate at full processing capacity.For completeness, included in the comparison is also a non-backlogclearing (NBC) policy from the prior art [Nedevschi], where neitherbacklog clearing property holds because the policy only allowssingle-step increments within the set of available rates. In all casesthe ratio between idle power and active power is β=0.8.

FIG. 10 provides the following important indications:

-   -   Rate adaptation is well-worth pursuing given its capability to        achieve large energy savings, especially in the lower portion of        the load range.    -   The advantage of sleep-state exploitation over rate scaling in        the lower portion of the load range justifies the specification        of rate-adaptation schemes that combine the two approaches.    -   All strong backlog clearing policies outperform their weak        backlog clearing counterparts.    -   The PSBC policy clearly underperforms the SSBC and PPSBC        policies, implying that the application of traffic arrival        predictors should not be extended to the entire duration of the        operating interval.    -   The energy-saving performance of the SSBC and PPSBC policies is        at least as good as that of the pre-existing NBC policy.

To choose between the SSBC and PPSBC policies, their relative delayperformances are illustrated in FIG. 11, which also plots the averageand maximum delay for the NBC policy.

FIG. 11 shows that the SSBC and PPSBC policies keep the maximum delaywithin the 11 ms bound of eq. (11), whereas the NBC policy violates it(although not by large amounts in this experiment). The PPSBC policyconsistently outperforms the SSBC policy with respect to both averageand maximum delay, by margins that are related to the ratio between thetransition time δ, over which the PPSBC prediction is projected, and theminimum hold time h. For simplicity, in the remainder of this sectionare presented results only for the PPSBC policy, which outperforms allother policies under state transition assumptions that are consistentwith currently available technologies. However, it should be understoodthat, as the state transition technology improves over time offeringshorter and shorter transition times, the delay performance distinctionwill sometime become irrelevant, making the simplest policy (SSBC) byfar the most appealing to implement.

Next is depicted the best minimum capacity to be made available in arate-adaptation scheme that combines rate scaling with sleep-stateexploitation. Referring to FIG. 12 there may be seen the energy-savingperformance of the PPSBC rate-adaptation scheme with 2 Gbps, 5 Gbps, and7 Gbps minimum capacity. The plot also includes the optimal rate-scalingscheme (PPSBC state-setting policy with 2 Gbps minimum capacity) and thesleep-state exploitation (SSE) scheme.

FIG. 12 confirms expectations in that:

-   -   The criterion of eq. (7) for setting the minimum non-null        capacity proves optimal: in the lower portion of the load range        the PPSBC policy with r_(min)=5 Gbps yields higher energy        savings than the same policy with r_(min)=2 Gbps and r_(min)=7        Gbps.    -   The rate-adaptation scheme significantly outperforms the        underlying rate-scaling scheme in the lower portion of the load        range.    -   The rate-adaptation scheme outperforms the sleep-state        exploitation scheme over the entire load range.

Finally, referring to FIG. 13 there may be seen a comparison between thedelay performance of the PPSBC rate-adaptation scheme (RA-PPSBC) withthe delay performance of the underlying rate-scaling scheme (RS-PPSBC).

The curves of FIG. 13 show that the two versions of PPSBC fully overlapfor loads in excess of 5 Gbps, and that the rate-adaptation versioncomplies with the delay bound of eq. (11) over the entire load range.

Summarizing, rate-adaptation schemes for improving the energy efficiencyof packet networks have been addressed resulting in two contributions.First, it has been shown that by proper abstraction of network datapaths it is possible to create the conditions for the incrementalintroduction of rate-adaptive devices in practical networks. Second,there has been defined a class of state-setting policies for rateadaptation schemes that enforce tight deterministic bounds on the extradelay that the schemes may cause to network traffic at every node wherethey are deployed. Within that class, there have been disclosedstate-setting policies that effectively combine the best properties ofsleep-state exploitation and rate-scaling schemes, demonstrating thatthe implementation of rate adaptation in practical network equipment canbe improved over the rate adaptation methods existent in the prior art.

The present invention can be embodied in the form of methods andapparatuses for practicing those methods. The present invention can alsobe embodied in the form of program code embodied in tangible media, suchas magnetic recording media, optical recording media, solid statememory, floppy diskettes, CD-ROMs, hard drives, or any othermachine-readable storage medium, wherein, when the program code isloaded into and executed by a machine, such as a computer, the machinebecomes an apparatus for practicing the invention. The present inventioncan also be embodied in the form of program code, for example, whetherstored in a storage medium or loaded into and/or executed by a machine,wherein, when the program code is loaded into and executed by a machine,such as a computer, the machine becomes an apparatus for practicing theinvention. When implemented on a general-purpose processor, the programcode segments combine with the processor to provide a unique device thatoperates analogously to specific logic circuits.

It will be further understood that various changes in the details,materials, and arrangements of the parts which have been described andillustrated in order to explain the nature of this invention may be madeby those skilled in the art without departing from the scope of theinvention as expressed in the following claims.

It should be understood that the steps of the exemplary methods setforth herein are not necessarily required to be performed in the orderdescribed, and the order of the steps of such methods should beunderstood to be merely exemplary. Likewise, additional steps may beincluded in such methods, and certain steps may be omitted or combined,in methods consistent with various embodiments of the present invention.

Although the elements in the following method claims, if any, arerecited in a particular sequence with corresponding labeling, unless theclaim recitations otherwise imply a particular sequence for implementingsome or all of those elements, those elements are not necessarilyintended to be limited to being implemented in that particular sequence.

Reference herein to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment can be included in at least one embodiment of theinvention. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments necessarilymutually exclusive of other embodiments. The same applies to the term“implementation.” Numerous modifications, variations and adaptations maybe made to the embodiment of the invention described above withoutdeparting from the scope of the invention, which is defined in theclaims.

1. A method of establishing a rate-adaptation domain in a packet network having a plurality of connected network nodes not providing rate adaptation, the method comprising the steps of: partitioning said plurality of connected network nodes into subdomains, each subdomain having connecting interfaces with at least one other subdomain; identifying those rate independent connecting interfaces among said connecting interfaces wherein the integrity of the data transfers through said connecting interfaces is preserved independent of the operating states of those subdomains connected; identifying at least one subdomain for which all connecting interfaces are rate independent connecting interfaces; and operating said at least one subdomain as a rate independent subdomain.
 2. A method as claimed in claim 1 wherein said operating step comprises: receiving packets at an input interface from a domain immediately upstream to said rate-adaptation domain; establishing Quality-of-Service requirements associated with said received packets; processing said received packets in a processing unit; dispatching packets at an output interface to a domain immediately downstream from said rate-adaptation domain; establishing a set of state variables; controlling the operating state of said processing unit with said state variables so as to control the rate at which packets are dispatched over said output interface; selecting such state variables so as to satisfy a feasible departure curve; and further selecting such state variables so as to reduce energy consumption in said rate-adaptation domain.
 3. A method as claimed in claim 2 wherein said packet processing step comprises at least one of the group of parsing, modifying, and storing in a buffer queue.
 4. A method as claimed in claim 3 wherein said further selecting step selects state variables so as to place said processing unit in a sleep state.
 5. A method as claimed in claim 3 wherein said further selecting step selects state variables so as to adjust the clock rate of said processing unit.
 6. A method as claimed in claim 2 wherein said feasible departure curve comprises an optimal departure curve.
 7. A system having a rate-adaptation domain in a packet network, said packet network having a plurality of connected network nodes, the system comprising: a plurality of subdomains, each subdomain being a partitioned subset of said plurality of connected network nodes, each subdomain having connecting interfaces with at least one other subdomain; a set of rate independent connecting interfaces among said connecting interfaces wherein the integrity of the data transfers through said connecting interfaces is preserved independent of the operating states of those subdomains connected; at least one subdomain for which all connecting interfaces are rate independent connecting interfaces; and said at least one subdomain operating as a rate-adaptation subdomain.
 8. A system as claimed in claim 7 wherein said at least one subdomain further comprises: an input interface for receiving packets from a domain immediately upstream to said rate-adaptation domain; a set of Quality-of-Service requirements associated with said received packets; a processing unit for processing said received packets; an output interface for dispatching packets to a domain immediately downstream from said rate-adaptation domain; a set of state variables for controlling the operating state of said processing unit wherein said state variables control the rate at which packets are dispatched over said output interface; a subset of such state variables selected so as to satisfy a feasible departure curve; and a further subset of said subset of state variables selected so as to reduce energy consumption in said rate-adaptation domain.
 9. A system as claimed in claim 8 further comprising: a buffer queue wherein said processing unit may store packets which have been at least one of the set of parsed and modified by said processing unit.
 10. A system as claimed in claim 9 wherein said state variables control at least one of the group of setting said processing unit in a sleep state and adjusting the clock rate of said processing unit.
 11. A system as claimed in claim 8 wherein said feasible departure curve comprises an optimal departure curve. 